---
title: Overview
---

The `LangChain` integrations related to [Amazon AWS](https://aws.amazon.com/) platform.

First-party AWS integrations are available in the `langchain_aws` package.

<CodeGroup>
```bash pip
pip install langchain-aws
```

```bash uv
uv add langchain-aws
```
</CodeGroup>

And there are also some community integrations available in the `langchain_community` package with the `boto3` optional dependency.

<CodeGroup>
```bash pip
pip install langchain-community boto3
```

```bash uv
uv add langchain-community boto3
```
</CodeGroup>

## Chat models

### Bedrock Chat

>[Amazon Bedrock](https://aws.amazon.com/bedrock/) is a fully managed service that offers a choice of
> high-performing foundation models (FMs) from leading AI companies like `AI21 Labs`, `Anthropic`, `Cohere`,
> `Meta`, `Stability AI`, and `Amazon` via a single API, along with a broad set of capabilities you need to
> build generative AI applications with security, privacy, and responsible AI. Using `Amazon Bedrock`,
> you can easily experiment with and evaluate top FMs for your use case, privately customize them with
> your data using techniques such as fine-tuning and `Retrieval Augmented Generation` (`RAG`), and build
> agents that execute tasks using your enterprise systems and data sources. Since `Amazon Bedrock` is
> serverless, you don't have to manage any infrastructure, and you can securely integrate and deploy
> generative AI capabilities into your applications using the AWS services you are already familiar with.

See a [usage example](/oss/integrations/chat/bedrock).

```python
from langchain_aws import ChatBedrock
```

### Bedrock Converse
AWS Bedrock maintains a [Converse API](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_Converse.html)
that provides a unified conversational interface for Bedrock models. This API does not
yet support custom models. You can see a list of all
[models that are supported here](https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference.html).

<Info>
**We recommend the Converse API for users who do not need to use custom models. It can be accessed using [ChatBedrockConverse](https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock_converse.ChatBedrockConverse.html).**


</Info>

See a [usage example](/oss/integrations/chat/bedrock).

```python
from langchain_aws import ChatBedrockConverse
```

## LLMs

### Bedrock

See a [usage example](/oss/integrations/llms/bedrock).

```python
from langchain_aws import BedrockLLM
```

### Amazon API Gateway

>[Amazon API Gateway](https://aws.amazon.com/api-gateway/) is a fully managed service that makes it easy for
> developers to create, publish, maintain, monitor, and secure APIs at any scale. APIs act as the "front door"
> for applications to access data, business logic, or functionality from your backend services. Using
> `API Gateway`, you can create RESTful APIs and WebSocket APIs that enable real-time two-way communication
> applications. `API Gateway` supports containerized and serverless workloads, as well as web applications.
>
> `API Gateway` handles all the tasks involved in accepting and processing up to hundreds of thousands of
> concurrent API calls, including traffic management, CORS support, authorization and access control,
> throttling, monitoring, and API version management. `API Gateway` has no minimum fees or startup costs.
> You pay for the API calls you receive and the amount of data transferred out and, with the `API Gateway`
> tiered pricing model, you can reduce your cost as your API usage scales.

See a [usage example](/oss/integrations/llms/amazon_api_gateway).

```python
from langchain_community.llms import AmazonAPIGateway
```

### SageMaker Endpoint

>[Amazon SageMaker](https://aws.amazon.com/sagemaker/) is a system that can build, train, and deploy
> machine learning (ML) models with fully managed infrastructure, tools, and workflows.

We use `SageMaker` to host our model and expose it as the `SageMaker Endpoint`.

See a [usage example](/oss/integrations/llms/sagemaker).

```python
from langchain_aws import SagemakerEndpoint
```

## Embedding Models

### Bedrock

See a [usage example](/oss/integrations/text_embedding/bedrock).
```python
from langchain_aws import BedrockEmbeddings
```

### SageMaker Endpoint

See a [usage example](/oss/integrations/text_embedding/sagemaker-endpoint).
```python
from langchain_community.embeddings import SagemakerEndpointEmbeddings
from langchain_community.llms.sagemaker_endpoint import ContentHandlerBase
```

## Document loaders

### AWS S3 Directory and File

>[Amazon Simple Storage Service (Amazon S3)](https://docs.aws.amazon.com/AmazonS3/latest/userguide/using-folders.html)
> is an object storage service.
>[AWS S3 Directory](https://docs.aws.amazon.com/AmazonS3/latest/userguide/using-folders.html)
>[AWS S3 Buckets](https://docs.aws.amazon.com/AmazonS3/latest/userguide/UsingBucket.html)

See a [usage example for S3DirectoryLoader](/oss/integrations/document_loaders/aws_s3_directory).

See a [usage example for S3FileLoader](/oss/integrations/document_loaders/aws_s3_file).

```python
from langchain_community.document_loaders import S3DirectoryLoader, S3FileLoader
```

### Amazon Textract

>[Amazon Textract](https://docs.aws.amazon.com/managedservices/latest/userguide/textract.html) is a machine
> learning (ML) service that automatically extracts text, handwriting, and data from scanned documents.

See a [usage example](/oss/integrations/document_loaders/amazon_textract).

```python
from langchain_community.document_loaders import AmazonTextractPDFLoader
```

### Amazon Athena

>[Amazon Athena](https://aws.amazon.com/athena/) is a serverless, interactive analytics service built
>on open-source frameworks, supporting open-table and file formats.

See a [usage example](/oss/integrations/document_loaders/athena).

```python
from langchain_community.document_loaders.athena import AthenaLoader
```

### AWS Glue

>The [AWS Glue Data Catalog](https://docs.aws.amazon.com/en_en/glue/latest/dg/catalog-and-crawler.html) is a centralized metadata
> repository that allows you to manage, access, and share metadata about
> your data stored in AWS. It acts as a metadata store for your data assets,
> enabling various AWS services and your applications to query and connect
> to the data they need efficiently.

See a [usage example](/oss/integrations/document_loaders/glue_catalog).

```python
from langchain_community.document_loaders.glue_catalog import GlueCatalogLoader
```

## Vector stores

### Amazon OpenSearch Service

> [Amazon OpenSearch Service](https://aws.amazon.com/opensearch-service/) performs
> interactive log analytics, real-time application monitoring, website search, and more. `OpenSearch` is
> an open source,
> distributed search and analytics suite derived from `Elasticsearch`. `Amazon OpenSearch Service` offers the
> latest versions of `OpenSearch`, support for many versions of `Elasticsearch`, as well as
> visualization capabilities powered by `OpenSearch Dashboards` and `Kibana`.

We need to install several python libraries.

<CodeGroup>
```bash pip
pip install boto3 requests requests-aws4auth
```

```bash uv
uv add boto3 requests requests-aws4auth
```
</CodeGroup>

See a [usage example](/oss/integrations/vectorstores/opensearch#using-aos-amazon-opensearch-service).

```python
from langchain_community.vectorstores import OpenSearchVectorSearch
```

### Amazon DocumentDB Vector Search

>[Amazon DocumentDB (with MongoDB Compatibility)](https://docs.aws.amazon.com/documentdb/) makes it easy to set up, operate, and scale MongoDB-compatible databases in the cloud.
> With Amazon DocumentDB, you can run the same application code and use the same drivers and tools that you use with MongoDB.
> Vector search for Amazon DocumentDB combines the flexibility and rich querying capability of a JSON-based document database with the power of vector search.

#### Installation and Setup

See [detail configuration instructions](/oss/integrations/vectorstores/documentdb).

We need to install the `pymongo` python package.

<CodeGroup>
```bash pip
pip install pymongo
```

```bash uv
uv add pymongo
```
</CodeGroup>

#### Deploy DocumentDB on AWS

[Amazon DocumentDB (with MongoDB Compatibility)](https://docs.aws.amazon.com/documentdb/) is a fast, reliable, and fully managed database service. Amazon DocumentDB makes it easy to set up, operate, and scale MongoDB-compatible databases in the cloud.

AWS offers services for computing, databases, storage, analytics, and other functionality. For an overview of all AWS services, see [Cloud Computing with Amazon Web Services](https://aws.amazon.com/what-is-aws/).

See a [usage example](/oss/integrations/vectorstores/documentdb).

```python
from langchain_community.vectorstores import DocumentDBVectorSearch
```
### Amazon MemoryDB
[Amazon MemoryDB](https://aws.amazon.com/memorydb/) is a durable, in-memory database service that delivers ultra-fast performance. MemoryDB is compatible with Redis OSS, a popular open source data store,
enabling you to quickly build applications using the same flexible and friendly Redis OSS APIs, and commands that they already use today.

InMemoryVectorStore class provides a vectorstore to connect with Amazon MemoryDB.

```python
from langchain_aws.vectorstores.inmemorydb import InMemoryVectorStore

vds = InMemoryVectorStore.from_documents(
            chunks,
            embeddings,
            redis_url="rediss://cluster_endpoint:6379/ssl=True ssl_cert_reqs=none",
            vector_schema=vector_schema,
            index_name=INDEX_NAME,
        )
```
See a [usage example](/oss/integrations/vectorstores/memorydb).

## Retrievers

### Amazon Kendra

> [Amazon Kendra](https://docs.aws.amazon.com/kendra/latest/dg/what-is-kendra.html) is an intelligent search service
> provided by `Amazon Web Services` (`AWS`). It utilizes advanced natural language processing (NLP) and machine
> learning algorithms to enable powerful search capabilities across various data sources within an organization.
> `Kendra` is designed to help users find the information they need quickly and accurately,
> improving productivity and decision-making.

> With `Kendra`, we can search across a wide range of content types, including documents, FAQs, knowledge bases,
> manuals, and websites. It supports multiple languages and can understand complex queries, synonyms, and
> contextual meanings to provide highly relevant search results.

We need to install the `langchain-aws` library.

<CodeGroup>
```bash pip
pip install langchain-aws
```

```bash uv
uv add langchain-aws
```
</CodeGroup>

See a [usage example](/oss/integrations/retrievers/amazon_kendra_retriever).

```python
from langchain_aws import AmazonKendraRetriever
```

### Amazon Bedrock (Knowledge Bases)

> [Knowledge bases for Amazon Bedrock](https://aws.amazon.com/bedrock/knowledge-bases/) is an
> `Amazon Web Services` (`AWS`) offering which lets you quickly build RAG applications by using your
> private data to customize foundation model response.

We need to install the `langchain-aws` library.

<CodeGroup>
```bash pip
pip install langchain-aws
```

```bash uv
uv add langchain-aws
```
</CodeGroup>

See a [usage example](/oss/integrations/retrievers/bedrock).

```python
from langchain_aws import AmazonKnowledgeBasesRetriever
```

## Tools

### AWS Lambda

>[`Amazon AWS Lambda`](https://aws.amazon.com/pm/lambda/) is a serverless computing service provided by
> `Amazon Web Services` (`AWS`). It helps developers to build and run applications and services without
> provisioning or managing servers. This serverless architecture enables you to focus on writing and
> deploying code, while AWS automatically takes care of scaling, patching, and managing the
> infrastructure required to run your applications.

We need to install `boto3` python library.

<CodeGroup>
```bash pip
pip install boto3
```

```bash uv
uv add boto3
```
</CodeGroup>

See a [usage example](/oss/integrations/tools/awslambda).

## Memory

### AWS DynamoDB

>[AWS DynamoDB](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/dynamodb/index.html)
> is a fully managed `NoSQL` database service that provides fast and predictable performance with seamless scalability.

We have to configure the [AWS CLI](https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-configure.html).

We need to install the `boto3` library.

<CodeGroup>
```bash pip
pip install boto3
```

```bash uv
uv add boto3
```
</CodeGroup>

See a [usage example](/oss/integrations/memory/aws_dynamodb).

```python
from langchain_community.chat_message_histories import DynamoDBChatMessageHistory
```

## Graphs

### Amazon Neptune

>[Amazon Neptune](https://aws.amazon.com/neptune/)
> is a high-performance graph analytics and serverless database for superior scalability and availability.

For the Cypher and SPARQL integrations below, we need to install the `langchain-aws` library.

<CodeGroup>
```bash pip
pip install langchain-aws
```

```bash uv
uv add langchain-aws
```
</CodeGroup>

### Amazon Neptune with Cypher

See a [usage example](/oss/integrations/graphs/amazon_neptune_open_cypher).

```python
from langchain_aws.graphs import NeptuneGraph
from langchain_aws.graphs import NeptuneAnalyticsGraph
from langchain_aws.chains import create_neptune_opencypher_qa_chain
```

### Amazon Neptune with SPARQL

See a [usage example](/oss/integrations/graphs/amazon_neptune_sparql).

```python
from langchain_aws.graphs import NeptuneRdfGraph
from langchain_aws.chains import create_neptune_sparql_qa_chain
```



## Callbacks

### Bedrock token usage

```python
from langchain_community.callbacks.bedrock_anthropic_callback import BedrockAnthropicTokenUsageCallbackHandler
```

### SageMaker Tracking

>[Amazon SageMaker](https://aws.amazon.com/sagemaker/) is a fully managed service that is used to quickly
> and easily build, train and deploy machine learning (ML) models.

>[Amazon SageMaker Experiments](https://docs.aws.amazon.com/sagemaker/latest/dg/experiments.html) is a capability
> of `Amazon SageMaker` that lets you organize, track,
> compare and evaluate ML experiments and model versions.

We need to install several python libraries.

<CodeGroup>
```bash pip
pip install google-search-results sagemaker
```

```bash uv
uv add google-search-results sagemaker
```
</CodeGroup>

See a [usage example](/oss/integrations/callbacks/sagemaker_tracking).

```python
from langchain_community.callbacks import SageMakerCallbackHandler
```

## Chains

### Amazon Comprehend Moderation Chain

>[Amazon Comprehend](https://aws.amazon.com/comprehend/) is a natural-language processing (NLP) service that
> uses machine learning to uncover valuable insights and connections in text.


We need to install the `boto3` and `nltk` libraries.

<CodeGroup>
```bash pip
pip install boto3 nltk
```

```bash uv
uv add boto3 nltk
```
</CodeGroup>

See a [usage example](https://python.langchain.com/v0.1/docs/guides/productionization/safety/amazon_comprehend_chain/).

```python
from langchain_experimental.comprehend_moderation import AmazonComprehendModerationChain
```
